Network Working Group M. Rose
Request for Comments: 1187 Performance Systems International, Inc.
K. McCloghrie
Hughes LAN Systems
J. Davin
MIT Laboratory for Computer Science
October 1990
Bulk Table Retrieval with the SNMP
1. Status of this Memo
This memo reports an interesting family of algorithms for bulk table
retrieval using the Simple Network Management Protocol (SNMP). This
memo describes an Experimental Protocol for the Internet community,
and requests discussion and suggestions for improvements. This memo
does not specify a standard for the Internet community. Please refer
to the current edition of the "IAB Official Protocol Standards" for
the standardization state and status of this protocol. Distribution
of this memo is unlimited.
Table of Contents
1. Status of this Memo .................................. 1
2. Abstract ............................................. 1
3. Bulk Table Retrieval with the SNMP ................... 2
4. The Pipelined Algorithm .............................. 3
4.1 The Maximum Number of Active Threads ................ 4
4.2 Retransmissions ..................................... 4
4.3 Some Definitions .................................... 4
4.4 Top-Level ........................................... 5
4.5 Wait for Events ..................................... 6
4.6 Finding the Median between two OIDs ................. 8
4.7 Experience with the Pipelined Algorithm ............. 10
4.8 Dynamic Range of Timeout Values ..................... 10
4.9 Incorrect Agent Implementations ..................... 10
5. The Parallel Algorithm ............................... 11
5.1 Experience with the Parallel Algorithm .............. 11
6. Acknowledgements ..................................... 11
7. References ........................................... 12
Security Considerations.................................. 12
Authors' Addresses....................................... 12
2. Abstract
This memo reports an interesting family of algorithms for bulk table
retrieval using the Simple Network Management Protocol (RFC 1157) [1].
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The reader is expected to be familiar with both the Simple Network
Management Protocol and SNMP's powerful get-next operator. Please
send comments to: Marshall T. Rose .
3. Bulk Table Retrieval with the SNMP
Empirical evidence has shown that SNMP's powerful get-next operator is
effective for table traversal, particularly when the management
station is interested in well-defined subsets of a particular table.
There has been some concern that bulk table retrieval can not be
efficiently accomplished using the powerful get-next operator. Recent
experience suggests otherwise.
In the simplest case, using the powerful get-next operator, one can
traverse an entire table by retrieving one object at a time. For
example, to traverse the entire ipRoutingTable, the management station
starts with:
get-next (ipRouteDest)
which might return
ipRouteDest.0.0.0.0
The management station then continues invoking the powerful get-next
operator, using the value provided by the previous response, e.g.,
get-next (ipRouteDest.0.0.0.0)
As this sequence continues, each column of the ipRoutingTable can be
retrieved, e.g.,
get-next (ipRouteDest.192.33.4.0)
which might return
ipRouteIfIndex.0.0.0.0
Eventually, a response is returned which is outside the table, e.g.,
get-next (ipRouteMask.192.33.4.0)
which might return
ipNetToMediaIfIndex.192.33.4.1
So, using this scheme, O(rows x columns) management operations are
required to retrieve the entire table.
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This approach is obviously sub-optimal as the powerful get-next
operator can be given several operands. Thus, the first step is to
retrieve an entire row of the table with each operation, e.g.,
get-next (ipRouteDest, ipRouteIfIndex, ..., ipRouteMask)
which might return
ipRouteDest.0.0.0.0
ipRouteIfIndex.0.0.0.0
ipRouteMask.0.0.0.0
The management station can then continue invoking the powerful get-
next operator, using the results of the previous operation as the
operands to the next operation. Using this scheme O(rows) management
operations are required to retrieve the entire table.
Some have suggested that this is a weakness of the SNMP, in that
O(rows) serial operations is time-expensive. The problem with such
arguments however is that implicit emphasis on the word "serial". In
fact, there is nothing to prevent a clever management station from
invoking the powerful get-next operation several times, each with
different operands, in order to achieve parallelism and pipelining in
the network. Note that this approach requires no changes in the
SNMP, nor does it add any significant burden to the agent.
4. The Pipelined Algorithm
Let us now consider an algorithm for bulk table retrieval with the
SNMP. In the interests of brevity, the "pipelined algorithm" will
retrieve only a single column from the table; without loss of
generality, the pipelined algorithm can be easily extended to
retrieve all columns.
The algorithm operates by adopting a multi-threaded approach: each
thread generates its own stream of get-next requests and processes
the resulting stream of responses. For a given thread, a request
will correspond to a different row in the table.
Overall retrieval efficiency is improved by being able to keep
several transactions in transit, and by having the agent and
management station process transactions simultaneously.
The algorithm will adapt itself to varying network conditions and
topologies as well as varying loads on the agent. It does this both
by varying the number of threads that are active (i.e., the number of
transactions that are being processed and in transit) and by varying
the retransmission timeout. These parameters are varied based on the
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transaction round-trip-time and on the loss/timeout of transactions.
4.1. The Maximum Number of Active Threads
One part of the pipelined algorithm which must be dynamic to get best
results is the determination of how many threads to have active at a
time. With only one thread active, the pipelined algorithm
degenerates to the serial algorithm mentioned earlier. With more
threads than necessary, there is a danger of overrunning the agent,
whose only recourse is to drop requests, which is wasteful. The
ideal number is just enough to have the next request arrive at the
agent, just as it finishes processing the previous request. This
obviously depends on the round-trip time, which not only varies
dynamically depending on network topology and traffic-load, but can
also be different for different tables in the same agent.
With too few threads active, the round-trip time barely increases
with each increase in the number of active threads; with too many,
the round-trip time increases by the amount of time taken by the
agent to process one request. The number is dynamically estimated by
calculating the round-trip-time divided by the number of active
threads; whenever this value takes on a new minimum value, the limit
on the number of threads is adjusted to be the number of threads
active at the time the corresponding request was sent (plus one to
allow for loss of requests).
4.2. Retransmissions
When there are no gateways between the manager and agent, the
likelihood of in-order arrival of requests and responses is quite
high. At present, the decision to retransmit is based solely on the
timeout. One possible optimization is for the manager to remember
the order in which requests are sent, and correlate this to incoming
responses. If one thread receives a response before another thread
which sent an earlier request, then lossage could be assumed, and a
retransmission made immediately.
4.3. Some Definitions
To begin, let us define a "thread" as some state information kept in
the management station which corresponds to a portion of the table to
be retrieved. A thread has several bits of information associated
with it:
(1) the range of SNMP request-ids which this thread can use,
along with the last request-id used;
(2) last SNMP message sent, the number of times it has been
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(re)sent, the time it was (re)sent;
(3) the inclusive lower-bound and exclusive upper-bound of
the object-instance for the portion of the table that
this thread will retrieve, along with the current
object-instance being used;
(4) the number of threads which were active at the time it
was last sent;
When a thread is created, it automatically sends a get-next message
using its inclusive lower-bound OID. Further, it is placed at the
end of the "thread queue".
Let us also define an OID as a concrete representation of an object
identifier which contains two parts:
(1) the number of sub-identifiers present, "nelem";
(2) the sub-identifiers themselves in an array, "elems",
indexed from 1 up to (and including) "nelem".
4.4. Top-Level
The top-level consists of starting three threads, and then entering a
loop. As long as there are existing threads, the top-level waits for
events (described next), and then acts upon the incoming messages.
For each thread which received a response, a check is made to see if
the OID of the response is greater than or equal to the exclusive
upper-bound of the thread. If so, the portion of the table
corresponding to the thread has been completely retrieved, so the
thread is destroyed.
Otherwise, the variable bindings in the response are stored.
Following this, if a new thread should be created, then the portion
of the table corresponding to the thread is split accordingly.
Regardless, another powerful get-next operator is issued on behalf of
the thread.
The initial starting positions (column, column.127, and column.192),
were selected to form optimal partitions for tables which are indexed
by IP addresses. The algorithm could easily be modified to choose
other partitions; however, it must be stressed that the current
choices work for any tabular object.
pipelined_algorithm (column)
OID column;
{
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timeout ::= some initial value;
start new thread for [column, column.127);
start new thread for [column.127, column.192);
start new thread for [column.192, column+1);
while (threads exist) {
wait for events;
foreach (thread that has an incoming message,
examined in order from the thread queue) {
OID a;
if (message's OID >= thread's upper-bound) {
destroy thread;
continue;
}
store variable-bindings from message;
if (number of simultaneous threads does NOT
exceed a maximum number
&& NOT backoff
&& (a ::= oid_median (message's OID,
thread's
upper-bound))) {
start new thread for [a, thread's upper-bound);
thread's upper-bound ::= a;
place thread at end of thread queue;
backoff ::= TRUE;
}
do another get-next for thread;
}
}
}
4.5. Wait for Events
Waiting for events consists of waiting a small amount of time or
until at least one message is received.
Any messages encountered are then collated with the appropriate
thread. In addition, the largest round-trip time for
request/responses is measured, and the maximum number of active
threads is calculated.
Next, the timeout is adjusted: if no responses were received, then
the timeout is doubled; otherwise, a timeout-adjustment is calculated
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as 1.5 times the largest observed round-trip time. If the timeout-
adjustment is greater than the current timeout, the current timeout
is set to the timeout-adjustment. Otherwise, the current timeout is
averaged with the timeout-adjustment.
Finally, if at least one thread did not receive a response, then the
thread is identified which has waited the longest. If the elapsed
time (with noise factor) since the last request (or retransmission)
is greater than the current timeout value, another retransmission is
attempted.
wait for events ()
{
backoff ::= TRUE, maxrtt ::= 0;
find the thread which has been waiting the longest
for a response;
timedelta = timeout
- time since request was sent for thread;
wait up to timedelta seconds or until some messages arrive;
if (least one message arrived) {
discard any messages which aren't responses;
foreach (response which corresponds to a thread) {
if (the response is a duplicate)
drop it and continue;
if (this response is for a message that was
not retransmitted) {
if (the round-trip time is larger than maxrtt)
set maxrtt to the new round-trip time;
if (round-trip time / number of active threads
< minimum previous round-trip time / number
of active threads) {
set new minimum round-trip time per number of
active threads
set new maximum number of threads
}
backoff ::= FALSE;
}
}
}
if (backoff)
double timeout;
elsif (maxrtt > 0) {
timeadjust ::= maxrtt * 3 / 2;
if (timeadjust > timeout)
timeout ::= timeadjust; backoff ::= TRUE;
else
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timeout ::= (timeout + timeadjust) / 2;
}
if (timeout exceeds some threshold)
set timeout to that threshold;
elsif (timeout is smaller than some threshold)
set timeout to that threshold;
if (at least one thread didn't receive a response) {
find the thread which has been waiting the longest
for a response,
and determine the elapsed time since a message
was sent;
if (the elapsed time with noise is greater than timeout) {
if (the number of retransmissions for this thread
exceeds a threshold)
abort the algorithm;
retransmit the request;
backoff ::= TRUE;
}
}
}
4.6. Finding the Median between two OIDs
The object identifier space is neither uniform nor continuous. As
such, it is not always possible to choose an object identifier which
is lexicographically-between two arbitrary object identifiers. In
view of this, the pipelined algorithm makes a best-effort attempt.
Starting from the beginning, each sub-identifier of the two OIDs is
scanned until a difference is encountered. At this point there are
several possible conditions:
(1) The upper OID has run out of sub-identifiers. In this
case, either the two OIDs are are identical or the lower
OID is greater than the upper OID (an interface error),
so no OID is returned.
(2) The lower OID has run out of sub-identifiers. In this
case, the first subsequent non-zero sub-identifier from
the upper OID is located. If no such sub-identifier is
found, then no OID exists between the lower and upper
OIDs, and no OID is returned. Otherwise, a copy of the
upper OID is made, but truncated at this non-zero
sub-identifier, which is subsequently halved, and the
resulting OID is returned.
(3) Otherwise, a copy of the lower OID is made, but truncated
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at the point of difference. This last sub-identifier is
then set to the arithmetic mean of the difference. In
the case where the difference is only 1 (so the last
sub-identifier remains the same) then a new sub-
identifier is added, taking care to be larger than a
possible sub-identifier present in the lower OID.
Regardless, the resulting OID is returned.
oid_median (lower, upper)
OID lower,
upper;
{
for (i ::= 1; i < upper:nelem; i++) {
if (i > lower:nelem) {
while (upper:elems[i] == 0)
if (++i > upper:nelem)
return NULL;
median ::= copy of upper;
median:nelem ::= i;
median:elems[i] ::= upper:elems[i] / 2;
return median;
}
if (lower:elems[i] == upper:elems[i])
continue;
median ::= copy of lower;
median:nelem ::= i;
median:elems[i] ::= (lower:elems[i]+upper:elems[i])/2;
if (median:elems[i] == lower:elems[i]) {
median:nelem ::= (i + 1);
if (lower:nelem < i)
median:elems[median:nelem] ::= 127;
elsif ((x ::= lower:elems[i + 1]) >= 16383)
median:elems[median:nelem] ::= x + 16383;
elsif (x >= 4095)
median:elems[median:nelem] ::= x + 4095;
elsif (x >= 1023)
median:elems[median:nelem] ::= x + 1023;
elsif (x >= 255)
median:elems[median:nelem] ::= x + 255;
else median:elems[median:nelem] ::=
(x / 2) + 128;
}
return median;
}
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return NULL;
}
4.7. Experience with the Pipelined Algorithm
This pipelined algorithm has been implemented and some
experimentation has been performed. It would be premature to provide
extensive performance figures at this time, as the pipelined
algorithm is still being tuned, and is implemented only in a
prototype setting. However, on tables of size O(2500), performance
of 121 entries/second has been observed. In contrast, the serial
algorithm has performance of roughly 56 entries/second for the same
table.
4.8. Dynamic Range of Timeout Values
It should be noted that the pipelined algorithm takes a simplistic
approach with the timeout value: it does not maintain a history of
the value and act accordingly.
For example, if the timeout reaches the maximum timeout limit, and
then latches for some period of time, this indicates a resource
(either the network or the agent) is saturated. Unfortunately, a
solution is difficult: an elegant approach would be to combine two
threads (but it is quite possible that no two consecutive threads
exist when this determination is made). Another approach might be to
delay the transmission for threads which are ready to issue a new
get-next operation.
Similarly, if the timeout drops to the minimum value and subsequently
latches, more threads should be started.
4.9. Incorrect Agent Implementations
An interesting result is that many agents do not properly implement
the powerful get-next operator. In particular, when a get-next
request contains an operand with an arbitrarily-generated suffix,
some agent implementations will handle this improperly, and
ultimately return a result which is lexicographically less than the
operand!
A typical cause of this is when the instance-identifier for a
columnar object is formed by a MAC or IP address, so each octet of
the address forms a sub-identifier of the instance-identifier. In
such circumstances, the incorrect agent implementations compare
against only the least significant octet of the sub-identifiers in
the operand, instead of the full value of the sub-identifiers.
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Upon encountering such an interaction, the pipelined algorithm
implementation declares the thread dead (noting a possible gap in the
table), and continues.
5. The Parallel Algorithm
One interesting optimization is to view the problem in two steps: in
the first step, one column of the table is traversed to determine the
full range of instances identifiers meaningful in the table.
(Indeed, although as described above, the pipelined algorithm
retrieves a single column, the prototype implementation can retrieve
multiple columns). In the second step, additional columns can be
retrieved using the SNMP get operation, since the instance
identifiers are already known. Further, the manager can dynamically
determine how many variables can be placed in a single SNMP get
operation in order to minimize the number of requests. Of course,
since the agent's execution of the get operation is often less
expensive than execution of the powerful get-next operation, when
multiple columns are request, this two-step process requires less
execution time on the agent.
A second algorithm can be developed, the "parallel algorithm". At
present, each thread is mapped onto a single SNMP operation. A
powerful insight is to suggest mapping several threads onto a single
SNMP operation: the manager must dynamically determine how many
variables can be placed in a single powerful get-next operation.
This has the advantage of reducing traffic, at the expense of
requiring the agent to utilize more resources for each request.
Earlier it was noted that the serial retrieval of objects could be
viewed as a degenerate case of the pipelined algorithm, in which the
number of active threads was one. Similarly, the pipelined algorithm
is a special case of the parallel algorithm, in which the number of
threads per SNMP operation is one.
5.1. Experience with the Parallel Algorithm
The parallel algorithm has been implemented and some experimentation
has been performed. It would be premature to provide extensive
performance figures at this time, as the algorithm is still being
tuned, and is implemented only in a prototype setting. However, on
tables of size O(2500), performance of 320 entries/second has been
observed, a performance improvement of 571% over the serial
algorithm.
6. Acknowledgements
A lot of the ideas on pipelining are motivated by Van Jacobson's work
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on adaptive timers in TCP. The parallelization modifications were
originally suggested by Jeffrey D. Case.
Finally, the comments of the following individual is acknowledged:
Frank Kastenholz, Racal-Interlan
7. References
[1] Case, J., Fedor, M., Schoffstall, M., and J. Davin, Simple
Network Management Protocol (SNMP), RFC 1157, SNMP Research,
Performance Systems International, Performance Systems
International, MIT Laboratory for Computer Science, May 1990.
Security Considerations
Security issues are not discussed in this memo.
Authors' Addresses
Marshall T. Rose
PSI, Inc.
PSI California Office
P.O. Box 391776
Mountain View, CA 94039
Phone: (415) 961-3380
EMail: mrose@PSI.COM
Keith McCloghrie
Hughes LAN Systems
1225 Charleston Road
Mountain View, CA 94043
Phone: (415) 966-7934
EMail: KZM@HLS.COM
James R. Davin
MIT Laboratory for Computer Science, NE43-507
545 Technology Square
Cambridge, MA 02139
Phone: (617) 253-6020
EMail: jrd@ptt.lcs.mit.edu
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